scholarly journals Neural response variability and divisive normalization

2018 ◽  
Author(s):  
Ruben Coen-Cagli ◽  
Selina S Solomon
Keyword(s):  
Neural Activity ◽  
Response Variability ◽  
Single Trial ◽  
Perceptual Judgments ◽  
Cortical Responses ◽  
Divisive Normalization ◽  
Excitatory Stimulus ◽  
The Impact ◽  
Direct Quantification ◽  
General Reduction

Cortical responses to repeated presentations of a stimulus are variable. This variability is sensitive to experimental manipulations that are also known to engage divisive normalization: a widespread description of neural activity as the ratio of a numerator (the excitatory stimulus drive) and denominator (the normalization signal). Yet, we lack a framework to quantify the effects of normalization on response variability. We extended the standard normalization model, treating the numerator and denominator as stochastic quantities, and derived a method to infer the single-trial normalization strength, which cannot be measured directly. The model revealed a general reduction of response variability in macaque primary visual cortex for neurons that were more strongly normalized, and during trials in which normalization was inferred to be strong. This framework could enable a direct quantification of the impact of single-trial normalization on perceptual judgments, and can readily be applied to other sensory and non-sensory factors.

scholarly journals The influence of the dynamics and the level of maturity of the cortical functions as a prerequisite for the development of speech in children

2019 ◽  
Vol 147 (3-4) ◽  
pp. 199-204
Author(s):  
Maja Davidovic ◽  
Jadranka Otasevic ◽  
Nada Dobrota-Davidovic ◽  
Ivana Petronic ◽  
Dragomir Davidovic ◽  
...  
Keyword(s):  
Motor Control ◽  
Evoked Potentials ◽  
Physiological Changes ◽  
Articulation Disorder ◽  
Significant Difference ◽  
Cortical Responses ◽  
Quasi Experimental ◽  
Waveform Amplitude ◽  
And Control ◽  
The Impact

Introduction/Objective. The development of speech is the result of interaction of different systems of the cortex, which gradually acquires the ability of phonological presentation and motor control, in the presence of a series of physical and physiological changes in the morphology of the articulation system. The objective of the study was to examine the impact of laterality and cortical responses on the development of speech in children. Methods. Research is a quasi-experimental design with two groups. The sample covered 60 children from Belgrade, of both sexes, ages 5.5?7 years, divided into two groups, experimental (30) and control (30). We used the following instruments: test for assessing laterality and ascertaining evoked potentials. Results. On the visual lateralization subtest there was a statistically significant difference (?2 = 7.56, p < 0.05) between the observed groups. The visual evoked potentials on all measured parameters gave a statistically significant difference between the groups: waveform cortical responses ? left (?2 = 30.00, df = 1, p < 0.05); cortical responses ? right (?2 = 6.667, df = 1 , p < 0.05); waveform amplitude ? left (?2 = 13.469, df = 1, p < 0.05); amplitude ? right (?2 = 40.00, df = 1, p < 0.05), somatosensory potentials (?2 = 18.261, df = 1, p <0.05); waveform amplitude (?2 = 12.000, df = 1, p < 0.05); waveform latency (?2 = 5.455, df = 1, p < 0.05). Conclusion. Visual laterality, as well as visual and somatosensory cortical responses to stimuli is better in children without the present articulation disorder, which could be used for timely prevention planning.

scholarly journals Neuronal response variability as a product of divisive normalization; neurobiological implications at a macroscale level

2020 ◽  
Vol 3 ◽  
pp. 34
Author(s):  
Kathy L. Ruddy ◽  
David M. Cole ◽  
Colin Simon ◽  
Marc T. Bächinger
Keyword(s):  
Potential Role ◽  
Neuronal Response ◽  
Quantitative Model ◽  
Response Variability ◽  
Sensory Cortex ◽  
Neural Oscillations ◽  
Neural Encoding ◽  
Approach Response ◽  
Divisive Normalization

The occurrence of neuronal spikes recorded directly from sensory cortex is highly irregular within and between presentations of an invariant stimulus. The traditional solution has been to average responses across many trials. However, with this approach, response variability is downplayed as noise, so it is assumed that statistically controlling it will reveal the brain’s true response to a stimulus. A mounting body of evidence suggests that this approach is inadequate. For example, experiments show that response variability itself varies as a function of stimulus dimensions like contrast and state dimensions like attention. In other words, response variability has structure, is therefore potentially informative and should be incorporated into models which try to explain neural encoding. In this article we provide commentary on a recently published study by Coen-Cagli and Solomon that incorporates spike variability in a quantitative model, by explaining it as a function of divisive normalization. We consider the potential role of neural oscillations in this process as a potential bridge between the current microscale findings and response variability at the mesoscale/macroscale level.

scholarly journals LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Cyril R. Pernet ◽  
Nicolas Chauveau ◽  
Carl Gaspar ◽  
Guillaume A. Rousselet
Keyword(s):  
Hierarchical Linear Modeling ◽  
Response Variability ◽  
Evoked Responses ◽  
Single Trial ◽  
Linear Modeling ◽  
Matlab Toolbox ◽  
Hierarchical Linear Modelling ◽  
Space And Time ◽  
Brain Responses ◽  
Linear Modelling

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.

scholarly journals Prestimulus feedback connectivity biases the content of visual experiences

2019 ◽  
Vol 116 (32) ◽  
pp. 16056-16061 ◽  
Author(s):  
Elie Rassi ◽  
Andreas Wutz ◽  
Nadia Müller-Voggel ◽  
Nathan Weisz
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Brain Regions ◽  
Stimulus Onset ◽  
Neural Excitability ◽  
Gamma Frequency ◽  
Face Area ◽  
Oscillatory Power ◽  
The Impact

Ongoing fluctuations in neural excitability and in networkwide activity patterns before stimulus onset have been proposed to underlie variability in near-threshold stimulus detection paradigms—that is, whether or not an object is perceived. Here, we investigated the impact of prestimulus neural fluctuations on the content of perception—that is, whether one or another object is perceived. We recorded neural activity with magnetoencephalography (MEG) before and while participants briefly viewed an ambiguous image, the Rubin face/vase illusion, and required them to report their perceived interpretation in each trial. Using multivariate pattern analysis, we showed robust decoding of the perceptual report during the poststimulus period. Applying source localization to the classifier weights suggested early recruitment of primary visual cortex (V1) and ∼160-ms recruitment of the category-sensitive fusiform face area (FFA). These poststimulus effects were accompanied by stronger oscillatory power in the gamma frequency band for face vs. vase reports. In prestimulus intervals, we found no differences in oscillatory power between face vs. vase reports in V1 or in FFA, indicating similar levels of neural excitability. Despite this, we found stronger connectivity between V1 and FFA before face reports for low-frequency oscillations. Specifically, the strength of prestimulus feedback connectivity (i.e., Granger causality) from FFA to V1 predicted not only the category of the upcoming percept but also the strength of poststimulus neural activity associated with the percept. Our work shows that prestimulus network states can help shape future processing in category-sensitive brain regions and in this way bias the content of visual experiences.

scholarly journals Spinal fMRI of Interoceptive Attention/Awareness in Experts and Novices

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keyvan Kashkouli Nejad ◽  
Motoaki Sugiura ◽  
Benjamin Thyreau ◽  
Takayuki Nozawa ◽  
Yuka Kotozaki ◽  
...  
Keyword(s):  
Neural Activity ◽  
Breathing Pattern ◽  
Body Parts ◽  
Imaging Studies ◽  
Spinal Segment ◽  
Sensory Experiences ◽  
Extended Training ◽  
Spinal Fmri ◽  
The Impact ◽  
First Time

Many disciplines/traditions that promote interoceptive (inner sensation of body parts) attention/awareness (IAA) train practitioners to both attend to and be aware of interoceptive sensory experiences in body parts. The effect of such practices has been investigated in previous imaging studies but limited to cerebral neural activity. Here, for the first time, we studied the impact of these practices on the spinal neural activity of experts and novices. We also attempted to clarify the effect of constant and deep breathing, a paradigm utilized in concentration practices to avoid mind wandering, on IAA-related spinal neural activity. Subjects performed IAA tasks with and without a deep and constant breathing pattern in two sessions. Results showed that neural activity in the spinal segment innervating the attended-to body area increased in experts (P=0.04) when they performed IAA and that this increase was significantly larger for experts versus novices in each of the sessions (P=0.024). The significant effects of IAA and expertise on spinal neural activity are consistent with and elaborate on previous reports showing similar effects on cerebral neural activity. As the spinal cord directly innervates body parts, the results might indicate that IAA has an instantaneous (possibly beneficial) effect on the physical body after extended training.

Decorrelation of Spiking Variability and Improved Information Transfer Through Feedforward Divisive Normalization

2012 ◽  
Vol 24 (4) ◽  
pp. 867-894 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Visual Cortex ◽  
Information Transfer ◽  
Response Variability ◽  
Signal Integration ◽  
Correlated Noise ◽  
Stimulus Parameter ◽  
Divisive Normalization ◽  
Optimal Linear ◽  
Large Groups ◽  
Simple Average

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease

2014 ◽  
Vol 44 (15) ◽  
pp. 3341-3356 ◽  
Author(s):  
R. C. Wolf ◽  
F. Sambataro ◽  
N. Vasic ◽  
M. S. Depping ◽  
P. A. Thomann ◽  
...  
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Motor Function ◽  
Neural Activity ◽  
Resting State ◽  
Brain Atrophy ◽  
Clinical Symptoms ◽  
Resting State Fmri ◽  
Cognitive Networks ◽  
The Impact

Background.Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's ‘resting state’ could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients.Method.Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and ‘biological parametric mapping’ analyses to investigate the impact of atrophy on neural activity.Results.Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition.Conclusions.This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

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